Gps data repair

ABSTRACT

Repairing GPS data is disclosed. Repairing GPS data includes repairing an effort, comprising determining that the effort includes inaccurate GPS data; and adjusting the effort using a repaired base map. Repairing GPs data includes repairing a segment, comprising determining an inaccurate shape data in the segment; and adjusting shape data for the segment based on a repaired base.

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/609,847 entitled TECHNIQUES TO IMPROVE ACCURACY OF GPS-BASED DATAfiled Mar. 12, 2012 which is incorporated herein by reference for allpurposes.

BACKGROUND OF THE INVENTION

A common problem with global positioning satellite (GPS) data is that itis often measured with error. The error included in measured GPS datamay detrimentally affect the comparison between a first set of GPS dataand a second set of GPS data such that even if the two sets of GPS datawere recorded along similar geographic paths, the two sets of GPS datamay not be determined to be sufficiently similar to each other.Furthermore, in the event that a set of GPS data was recorded during auser's physical activity across a geographic track, if the set of GPSdata was recorded with sufficient error, then computations regarding theuser's performance (e.g., speed, acceleration) during the physicalactivity may not be accurately determined.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a diagram showing an embodiment of a system for validatingsegments.

FIG. 2 is a diagram showing an embodiment of a segment validationserver.

FIG. 3 is a flow diagram showing an embodiment of a process ofvalidating a segment.

FIG. 4 is a flow diagram showing an embodiment of a process forperforming segment validation.

FIG. 5 is a flow diagram showing an embodiment of a process ofvalidating a segment.

FIG. 6 is a flow diagram showing an example of determining a validatedsegment.

FIG. 7 is a diagram showing an example of visual representations of asegment and efforts that match the segment.

FIG. 8 is a diagram showing an example of selecting a best candidateeffort.

FIG. 9 is a diagram showing the determination of a centroid based on aGPS data point of the best candidate effort.

FIG. 10 is a diagram showing a centroid determined based on a GPS datapoint of the best candidate effort.

FIG. 11 is a diagram showing various centroids determined based oncorresponding GPS data points of the best candidate effort.

FIG. 12 is a diagram showing determined centroids corresponding to allthe GPS data points of the best candidate effort.

FIG. 13 is a diagram showing a validated segment determined based on thedetermined centroids.

FIG. 14 is a diagram showing an embodiment of a system for repairing GPSdata.

FIG. 15 is a diagram showing an example of a GPS data repair server.

FIG. 16 is a flow diagram showing an embodiment of a process forrepairing an effort.

FIG. 17 is a flow diagram showing an embodiment of a process forrepairing GPS data.

FIG. 18 is a flow diagram showing an embodiment of a process forcreating a repaired base map.

FIG. 19 is a diagram showing an example of generating a portion of arepaired base map.

FIG. 20 is a flow diagram showing an embodiment of a process forrepairing a segment.

FIG. 21 is a flow diagram showing an embodiment of a process forrepairing a segment.

FIGS. 22A through 22F show examples of repairing a segment using process2100 of FIG. 21.

FIG. 23 is a flow diagram showing an embodiment of a process forrepairing an effort that is determined to include stuck GPS data points.

FIGS. 24A through 24D show examples of repairing an effort with stuckGPS data points using a process such as process 2300 of FIG. 23.

FIG. 25 is a flow diagram showing an embodiment of a process forrepairing an effort that is determined to include drifted GPS datapoints.

FIGS. 26A and 26B show examples of repairing an effort with drifted GPSdata points using a process such as process 2500 of FIG. 25.

FIG. 27 is a flow diagram showing an embodiment of a process forrepairing an effort that did not match a segment but should have matcheda segment.

FIGS. 28A and 28B provide examples of determining a best performanceportion of an unrepaired and a repaired effort, respectively.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

As used herein, a segment refers to a representation of a real-worldgeographical track (e.g., route, path, trail) that is of interest andcan be used as a reference (e.g., for comparison of athleticperformances along that track and/or for other applications). Forexample, a segment can be used to identify a popular hill climb forcyclists, a running route, or a hiking trail. In various embodiments, asegment may be automatically defined. For example, third party sourcesthat store information regarding common paths for various physicalactivities may be used to automatically define segments. In variousembodiments, a segment may be defined by a user. For example, a user maymake selections along a map at a user interface to draw out the segment.In another example of a user defined segment, a user may record aneffort and define at least a portion of the GPS data associated with theeffort as the segment. As used herein, an effort refers to a recordedseries of GPS data with timestamps (e.g., which can represent aninstance of a user's athletic performance or physical activity). In someembodiments, an effort is stored with auxiliary data (e.g., metrics ofthe activity with which the effort is associated). A segment may bestored so that, for example, it can be used as reference to which arecorded effort can be determined to match so that the effort can be, ifdesired, compared against one or more other recorded efforts that alsomatch the same segment. In various embodiments, an effort is determinedto match a segment by meeting matching criteria associated with thesegment. In various embodiments, an effort that matches a segment isreferred to as a “segment match” or a “matching effort to the segment.”In some embodiments, data indicating a matching relationship is storedfor each segment effort (i.e., matching effort) with respect to thesegment that the effort has been determined to match.

However, a stored segment may need to be updated over time because itmay have been defined with error. An example of a segment that may bedefined with an error is a segment that is derived from the GPS data ofa recorded effort. Specifically, recorded GPS data may include measuresof latitude and longitude (“Lat-Lng Data”) and time. Auxiliary data suchas a user's heart rate, air temperature, elevation above sea level,power output, or another physiological, performance, or environmentalparameter, for example, may be captured by the device at the same pointsin time as Lat-Lng Data. The GPS-enabled device that was used to recordthe effort may record GPS data with error, which is then passed to thesegment that is defined from the effort. There are several kinds oferrors that can corrupt the GPS data. For example, one type of error maybe caused by the GPS-enabled device losing connection with one or moreof the satellites that it needs to accurately track Lat-Lng Data.Another type of error may be caused by the device measuring data tooinfrequently to correctly pinpoint data being collected at all pointsand times. Yet another type of error may be caused by the device havinga limited degree of accuracy such that it misinterprets Lat-Lng Data byintroducing “drift” to the Lat-Lng Data. Furthermore, a stored segmentmay need to be updated over time because the geographic track that it isintended to represent may change over time. For example, the terrain ofa trail that a segment was originally defined to represent may shiftover time, which will cause the originally defined segment to become aless accurate representation.

Embodiments of validating segments are described herein. In variousembodiments, validating a segment comprises adjusting at least some GPSdata associated with (e.g., representing) the segment using aggregatedGPS data. In various embodiments, the aggregated GPS data includes theGPS data of one or more efforts that were determined to match thesegment. In some embodiments, validating the segment comprisesgenerating a new version of the segment based on adjusting the GPS dataassociated with the stored segment using the aggregated GPS data. Insome embodiments, the new version of the segment will replace the storedsegment (the previous version of the segment) because it is assumed tobe a more accurate representation of the geographic track that thestored segment was intended to represent. In some embodiments, the GPSdata of one or more efforts determined to match the new version of thesegment may be subsequently used to validate that segment. Subsequentvalidation of the segment may be iteratively performed.

FIG. 1 is a diagram showing an embodiment of a system for validatingsegments. In the example, system 100 includes device 102, network 104,and segment validation server 106. Network 104 includes high-speed datanetworks and/or telecommunications networks. Device 102 may communicateto segment validation server 106 over network 104. In some embodiments,system 100 may include more or fewer components than what is shown inthe example of FIG. 1.

Device 102 is a device that can record GPS data and/or other dataassociated with a physical activity. Device 102 can also be a device towhich GPS data and/or other data associated with a physical activity canbe uploaded or transferred. Examples of device 102 include, but are notlimited to: a GPS device (e.g., Garmin Forerunner® and Edge° devices,including Garmin Forerunner® 110, 205, 301, 305, 310XT, 405, 405CX, andGarmin Edge® 305, 605, 705, 500, 800), a mobile phone, such as a smartphone (e.g., an Android®-based device or Apple iPhone® device) includinga GPS recording application (e.g., MotionX®, Endomondo®, andRunKeeper®), a computer, a tablet device, and/or other general purposecomputing devices and/or specialized computing devices, which typicallyinclude a general processor, a memory or other storage component(s), anetwork or input/output (I/O) capability, and possibly integrated GPSfunctionality or support or an interface for a GPS device or GPSfunctionality.

In various embodiments, device 102 (or an application thereof) isconfigured to record GPS data and auxiliary data associated with aphysical activity during the activity. For example, auxiliary dataassociated with a physical activity may include physiological,environmental, and/or performance data. In some embodiments, device 102is configured to receive recorded GPS data and auxiliary data associatedwith a physical activity subsequent to the completion of the activity(e.g., such information is uploaded to device 102).

In some embodiments, the recorded GPS data and the auxiliary data thatrepresent an instance of a physical activity are referred to as an“effort.” Put another way, an effort is an instance of a physicalactivity and can be represented through its geographical information aswell as other metrics related to athletic performance. Examples of aphysical activity include cycling, running, and skiing. In someembodiments, GPS data includes a series of consecutive and discrete GPSdata points (e.g., latitude and longitude coordinates sometimes referredto as “Lat-Lng Data”) with a timestamp for each point. In someembodiments, auxiliary data includes, but is not limited to, elevation,heart rate, power/watts (e.g., energy expended), time, speed (e.g.,average and/or maximum speed per segment and/or route, in which averagespeed, for example, can be derived from time and GPS information),and/or cadence. Auxiliary data can be recorded at various granularities.For example, auxiliary data can correspond to each GPS data point, theentire activity (e.g., the auxiliary data includes averages of themetrics), or portions of the activity. As an example, one can use device102 on a bike ride. At the end of the bike ride, the user can review hisperformance with the recorded GPS data (e.g., through a user interfaceof device 102) to observe the geographical track that he traversed, howmuch energy he expended along the ride, how fast he finished it in,average speed, elevation-based metrics, and/or other metrics. In someembodiments, device 102 is configured to store the recorded GPS data andthe auxiliary data (e.g., effort) and/or send the effort information toserver 106. In some embodiments, device 102 is configured to present aninteractive user interface. The user interface may display GPS data andreceive selections (e.g., made by a user) with respect to the displays.In some embodiments, device 102 sends the selections that it receives tosegment validation server 106.

In some embodiments, a user interface may be presented at device 102. Insome embodiments, the user interface may be presented by segmentvalidation server 106 or by another component that is not shown in theexample of FIG. 1. The user interface is configured to receive a userdefinition of a segment. In some embodiments, the user interface isconfigured to display a visual representation of the GPS data of arecorded effort at the user interface using a map software application.The visual representation can be, for example, a series of flags or acontinuous line marked on a graphical map. In some embodiments, segmentvalidation server 106 is able to support a map at the user interface byincluding logic configured to interact with the Application ProgrammingInterface (API) of a map software/application (e.g., Google® Maps,MapQuest®, Bing® maps, and/or another mapping application/service). Insome embodiments, a user may define a segment using the visualrepresentation of the GPS information. In some embodiments, a startpoint and a finish (e.g., end) point along the visual representation ofthe GPS information are selected on the graphical map to define asegment. For example, a user may select (e.g., by dropping a marker orclicking) the start and/or finish points along the geographical trackthat he or she just traversed during a physical activity. The portion ofthe geographical track between the selected start and finish points isthus defined as a segment. In some embodiments, the start and finishpoints are stored with the defined segment. In some embodiments, theportion of the geographical track between the selected start and finishpoints is converted into an abstracted form and then stored in adatabase for storing segments. In some embodiments, the associated datathat corresponds to the defined segment is also stored with the segmentat the database.

In some embodiments, the user interface is configured to receive a userdefinition of a segment using selections on a map without a visualrepresentation of a recorded effort. In some embodiments, a graphicalmap (e.g., Google® Maps, MapQuest®) is presented and a series ofselections of points on the map to indicate the course of a segment isreceived. This series of selections of points on the map need not bebased on a recorded effort and can merely be any geographical track thatis of interest. For example, the selected segment can be a track that acyclist has rode over before but has not documented the ride or theselected segment can be a track that the cyclist would like to ride onin the future. The series of selections of points on the map can beconverted into a series of GPS data (e.g., Lat-Lng Data). In someembodiments, the series of GPS data is converted into an abstracted formand then stored as a segment.

In some embodiments, subsequent to a definition of a segment at the userinterface, stored efforts (e.g., sets of GPS data from past physicalactivities) are compared to the defined segment. In some embodiments,when an effort is compared against this defined segment, it isdetermined whether the effort matches the segment in part by checkingwhether the GPS data of the effort indicates that the start point and/orfinish point of the segment have been crossed. In some embodiments,identifiers associated with matching efforts are stored for eachsegment.

Segment validation server 106 is configured to validate a storedsegment. In various embodiments, segment validation server 106 isconfigured to validate a stored segment based on aggregated GPS dataassociated with the segment. In some embodiments, aggregated GPS dataassociated with the segment includes at least some of the efforts thathave been determined to match the segment. In some embodiments, segmentvalidation server 106 is configured to start validation of a storedsegment in response to a trigger to start the validation process. Inresponse to the trigger, in some embodiments, segment validation server106 is configured to use at least some of the GPS data associated withone or more efforts that have been determined to match the segment toadjust the GPS data associated with the segment. In some embodiments,adjusting the GPS data associated with the segment includes generating anew version of the segment. In some embodiments, the new version of thesegment may comprise a modified version of the set of GPS dataassociated with the previous version of the segment. In someembodiments, the new version of the segment may also be referred to asthe “validated segment.” In some embodiments, the validated segment willreplace the previous version of the segment. In some embodiments, storedefforts, if any, that match the validated segment are determined. Insome embodiments, segment validation server 106 is configured tovalidate the validated segment based on aggregated GPS data (e.g., GPSdata that matches the validated segment). In this manner, segmentvalidation server 106 is configured to iteratively, over time, validatea segment using aggregated GPS data associated with the segment (e.g.,GPS data associated with efforts that match the segment).

Validating a segment leverages a body of relevant user submitted GPSdata to generate a more accurate and/or more current representation ofthe intended geographic track. In some embodiments, only a portion ofthe relevant user submitted GPS data recorded by one or more GPS devicesthat are known to be more accurate may be selected to validate thesegment, to increase the accuracy of the validation process. Moreaccurate segments can provide better matches to recorded efforts andtherefore increase the probability that an effort recorded along acertain geographic track will be determined to match the segment definedto represent that geographic track.

FIG. 2 is a diagram showing an embodiment of a segment validationserver. In some embodiments, segment validation server 106 of system 100of FIG. 1 may be implemented using the example of FIG. 2. In someembodiments, a segment validation server may include more or fewercomponents than what is shown in the example of FIG. 2. The example ofthe segment validation server includes segments database 202, effortsdatabase 204, segment creation engine 206, segment matching engine 208,and segment validation engine 210. Each of segments database 202 andefforts database 204 may be implemented using one or more databases.Each of segment creation engine 206, segment matching engine 208, andsegment validation engine 210 may be implemented using one or both ofhardware and software.

Segments database 202 is configured to store segments. In someembodiments, segments database 202 is configured to store segmentsderived from recorded user efforts. In some embodiments, segmentsdatabase 202 is configured to store segments generated by userselections along a map application (independent of a recorded effort).In some embodiments, segments database 202 is configured to storesegments generated based on third party sources. In some embodiments, asegment is stored as a set of GPS data. In some embodiments, the set ofGPS data associated with a segment is converted into an abstracted form(e.g., minimum bounding rectangles) and the converted data is stored torepresent the segment. In some embodiments, each segment is stored witha set of metadata. For example, the set of metadata associated with eachsegment may include an identifier associated with the segment, a numberof times that a segment associated with the segment identifier has beenvalidated/refined (e.g., a version number of the segment associated withthe segment identifier), identifiers of stored efforts that have beendetermined to match the segment (e.g., within a recent period of time orsince the last time the segment was validated), a time at which thesegment associated with the identifier was originally created, and oneor more types of physical activity that are associated with the segment(e.g., running and biking) In some embodiments, if a segment has beenvalidated at least once previously, then at least one or more previousversions of the segment may be stored (but not used to match toefforts).

In some embodiments, each segment stored at segments database 202 isassociated with one or more matching criteria. If an effort isdetermined (e.g., by segment matching engine 208) to meet the criteriaassociated with a segment, then the effort is determined to match thesegment and data indicating the matching relationship between thesegment and the effort may be stored with the segment and/or the effort.For example, matching criteria associated with a segment may include athreshold degree of similarity that an effort has to meet in order tomatch the segment. In some embodiments, each segment stored at segmentsdatabase 202 is associated with the same set of matching criteria. Insome embodiments, different segments stored at segments database 202 maybe associated with different sets of matching criteria.

Segment creation engine 206 is configured to receive user definition ofsegments. In some embodiments, segment creation engine 206 is configuredto present a user interface associated with segment definition. In someembodiments, a user may submit to segment creation engine 206 a set ofGPS data associated with a recorded effort and segment creation engine206 is configured to present a visual representation of the effort atthe user interface. Then user selections associated with a start pointand end point along the visual representation of the GPS data associatedwith the effort are received and segment creation engine 206 isconfigured to store at least the portion of the GPS data associated withthe effort between the start point and the end point as a segment atsegments database 202, for example. In some embodiments, segmentcreation engine 206 is configured to present a map application (e.g.,associated with a third party service such as Google® maps) at the userinterface and receive one or more user selections (e.g., user clicksalong a path on the map) with respect to the map application. The one ormore user selections on the map application are then converted into aset of (e.g., GPS) data and stored as a segment at segments database202, for example. In some embodiments, segment creation engine 206 isconfigured to prompt the user (via the user interface) to input metadataassociated with the segment. For example, the user may be prompted toinput an identifier for the segment and one or more types of physicalactivity associated with the segment.

Efforts database 204 is configured to store user recorded and uploadedefforts. In some embodiments, an effort is recorded during a user'sphysical activity by a GPS-enabled device. Examples of a user's physicalactivity may include biking or running Examples of a GPS-enabled devicemay include a GPS-enabled smart phone with an application associatedwith recording GPS data installed or a GPS-enabled watch. For example, auser can wear a GPS-enabled device on his body as he performs a physicalactivity. The user can interact with the device to start and endrecording. While the device is in the recording mode, it may recorddiscrete data points, where each data point may be associated with a GPScoordinate (e.g., Lat-Lng coordinates), a time association, and one ormore physical, physiological, environmental, and/or performance relatedmetrics (e.g., heart rate, cadence, power, speed, acceleration). In someembodiments, a user interface may be provided for the user and the usermay upload a recorded effort using at least the user interface. Forexample, the user can establish a wired or wireless connection betweenthe GPS-enabled device and a computer to upload the data associated withthe recorded effort(s), direct the web browser installed at the computerto a web address associated with the user interface, and upload the dataassociated with the recorded effort(s) to the segment validation server.Furthermore, the user interface may prompt the user to provide metadatato be associated with an uploaded effort such as, for example, anidentifier, a type of physical activity associated with the effort, anidentifier associated with the type of device the effort was recordedwith, an identifier of a user associated with the effort, and additionalnotes. In some embodiments, the set of metadata associated with aneffort may be updated over time. For example, over time, an effort maybe determined to match different or additional segments.

In some embodiments, an effort stored at efforts database 204 is matchedagainst one or more segments stored at segments database 202. Forexample, subsequent to a user uploading a recorded effort to the segmentvalidation server, segment matching engine 208 is configured to comparethe effort against each of the segments stored at segments database 202,for example, to determine whether the effort matches one or moresegments. In some embodiments, an effort is determined to match asegment based on the set of GPS data associated with the effort meetingone or more matching criteria associated with the segment. In the eventthat an effort is determined to match a segment (e.g., stored atsegments database 202), the effort is then stored with an identifierassociated with the matching segment. As such, if efforts that match acertain segment are to be determined, then the efforts of effortsdatabase 204 can be searched for those that are associated with thesegment's identifier.

Segment matching engine 208 is configured to match efforts to storedsegments. In some embodiments, segment matching engine 208 is configuredto determine whether at least some efforts stored at efforts database204 match one or more segments stored at segments database 202. Segmentmatching engine 208 is configured to determine whether an effort meetsthe one or more matching criteria associated with a particular segment.In the event that segment matching engine 208 determines that an effortmatches a segment, then data indicating the matching relationshipbetween the segment and effort may be stored with the segment and/or theeffort.

Segment validation engine 210 is configured to validate segments. A goalof segment validation is to continuously improve the quality of asegment and to change the segment, if appropriate, to better reflect thetrue (e.g., real-world) geographical track that the segment is supposedto represent. In some embodiments, segment validation engine 210validates at least some of the segments stored at segments database 202.Segment validation engine 210 validates a segment based on aggregatedGPS data. In some embodiments, segment validation engine 210 performsvalidation on a segment that meets one or more validation criteria. Forexample, a validation criterion is that a predetermined period of timehas passed since the segment has been created or last validated. Anotherexample of a validation criterion is that a predetermined number ofefforts (e.g., recorded by a particular GPS-enabled device) has beendetermined to match the segment. Yet another example of a validationcriterion is that less than a predetermined threshold of efforts hasbeen determined to match the segment since the segment has been createdor last validated.

Once segment validation engine 210 determines that a stored segment hasmet at least one validation criterion, segment validation engine 210proceeds to validate the segment. In some embodiments, segmentvalidation engine 210 is configured to validate a stored segment basedon aggregated GPS data including at least the set of efforts that havebeen determined to match the segment. For example, segment validationengine 210 can query efforts database 204 for the efforts that have beendetermined to match the segment to be validated (e.g., based on metadatastored with the efforts). In some embodiments, segment validation engine210 is configured to use at least the obtained efforts (e.g., theefforts' corresponding sets of GPS data) that have been determined tomatch the segment to validate the segment. In some embodiments, segmentvalidation engine 210 is configured to generate a new version of thesegment based on the obtained set of efforts. In some embodiments,segment validation engine 210 is configured to replace the previousversion of the segment with the new version of the segment as a resultof the validation. In some embodiments, the new version of the segment,which is sometimes referred to as the “validated segment,” is storedwith at least some of the metadata (e.g., segment identifier) associatedwith the previous version of the segment so as to replace the previousversion. In some embodiments, the metadata associated with the newversion of the segment may be updated to include an incremented versionnumber. In some embodiments, segment validation engine 210 is configuredto match one or more efforts (from efforts database 204) to thevalidated segment. As a result of the validation, more, fewer, ordifferent efforts may be determined to match the segment. In variousembodiments, segment validation engine 210 is configured to iterativelyvalidate a segment based on aggregated GPS data. For example, the newversion of the segment (the validated segment) may eventually meet avalidation criterion and become validated by segment validation engine210 based on the efforts that have been determined to match that versionof the segment.

Because a segment may be originally derived from the GPS data associatedwith a recorded user effort, sometimes the error included in therecording of the effort may be passed onto the resulting segment. As aresult, certain efforts that were recorded with greater accuracy alongthe geographic track that a segment was intended to represent may not bedetermined to match the segment, which will negatively affect users'ability to compare performances along the same geographic track.However, by continuously updating/validating an existing segment usingat least aggregated user GPS data that is relevant to the segment (e.g.,matching efforts), the segment may be continually improved to moreaccurately reflect the current real-world shape of the geographic track.

FIG. 3 is a flow diagram showing an embodiment of a process ofvalidating a segment. In some embodiments, process 300 is implemented atsystem 100 of FIG. 1.

At 302, it is determined that a stored segment meets a validationcriterion. In some embodiments, segment validation for a stored segmentbegins when the segment meets a validation criterion. In someembodiments, one or more validation criterion may be user configured. Afirst example of a validation criterion is an elapse of a predeterminedtime interval since the segment was last validated or since thesegment's initial creation. A second example of a validation criterionis when less than a predetermined number of efforts are determined tomatch the segment within a predetermined period of time (e.g., the lackof matching efforts may indicate that the segment was created withsignificant error). A third example of a validation criterion is whenthe number of efforts that match the segment has reached a predeterminedthreshold. A fourth example of a validation criterion is when the numberof efforts recorded by a selected device (e.g., that is known to berelatively more accurate than other devices) that match the segment hasreached a predetermined threshold. A fifth example of a validationcriterion is receipt of an indication of a user selection to initiatethe validation process.

At 304, GPS data associated with the stored segment is adjusted usingaggregated GPS data. In various embodiments, sets of GPS data associatedwith the segment are aggregated and used to adjust the GPS dataassociated with the segment. In some embodiments, sets of GPS dataassociated with the segment comprise the GPS data associated with theefforts that have been determined to match the segment. In someembodiments, the sets of GPS data corresponding to the matching effortsare obtained and used to generate a new version of the segment. In someembodiments, the new version of the segment is used to replace theprevious version of the segment.

FIG. 4 is a flow diagram showing an embodiment of a process forperforming segment validation. In some embodiments, process 400 isperformed at system 100 of FIG. 1.

Process 400 shows that a segment may be repeatedly validated to furtherimprove the accuracy and quality of the segment. In some embodiments,process 400 is performed for each stored segment.

At 402, it is determined whether a segment is to be validated. In someembodiments, the segment is determined to be validated based on thesegment meeting at least one validation criterion. Examples ofvalidation criterion include an elapse of a predetermined time intervalsince a previous validation or creation of the segment, when the numberof efforts determined to match the segment reaches a predeterminedthreshold number, when the number of matching efforts is less than apredetermined number during a predetermined period of time, and when anindication associated with a user selection for the segment to bevalidated is received.

At 404, validation is performed on the stored segment. In someembodiments, aggregated GPS data associated with the segment is used toperform validation of the segment. In some embodiments, as a result ofthe validation, a new version of the segment is generated and used toreplace the previous version of the segment.

At 406, it is determined whether to stop validation of the segment. Forexample, the cycle of segment validation may be stopped in response to auser selection to stop the process or in response to the system losingpower. In the event that validation is determined to be stopped, process400 ends. Otherwise, in the event that validation is determined tocontinue, control passes to the start of 400. For example, after oneiteration of step 404, the new version of the segment is determined toreplace the previous version of the segment. Then when the nextiteration process 400 is performed, this new version of the segment isdetermined to be validated.

FIG. 5 is a flow diagram showing an embodiment of a process ofvalidating a segment. In some embodiments, process 500 is performed atsystem 100 of FIG. 1. In some embodiments, step 404 of process 400 ofFIG. 4 is performed using at least a portion of process 500.

Process 500 shows that efforts may be compared against a newly generatedvalidated segment (i.e., a new version of a segment generated fromvalidating a segment).

At 502, an indication to validate a stored segment is received. Forexample, the stored segment is determined to have met a validationcriterion.

At 504, a first plurality of sets of GPS data associated with the storedsegment is obtained. In some embodiments, the first plurality of sets ofGPS data comprises the sets of GPS data corresponding to the effortsthat have been determined to match the stored segment since the segmentwas initially created or was last validated.

At 506, a validated segment is generated based at least in part on thefirst plurality of sets of GPS data associated with the stored segment.In some embodiments, the validated segment comprises a new version ofthe segment generated based on the set of efforts that had beendetermined to match the segment. In some embodiments, the previousversion of the segment, which is the version of the segment prior to thecurrent validation process, is replaced by the new version of thesegment. In some embodiments, replacing a segment with a new version ofthe segment includes discarding the segment and storing the new versionof the segment with at least some of the metadata that was stored forthe discarded version. In some embodiments, the metadata associated withthe new version of the segment includes an incremented version number.

At 508, it is determined whether any of a second plurality of sets ofGPS data matches the validated segment. In some embodiments, the set ofstored efforts is compared against the new version of the segment todetermine which efforts match this new version. Sometimes, the validatedsegment (the new version of the segment) may differ from the previousversion of the segment enough such that efforts that did not previouslymatch the previous version of the segment may match this new versionand/or efforts that previously did match the previous version of thesegment may not match the new version of the segment. In someembodiments, identifiers of efforts that are determined to match the newversion of the segment are stored with the segment so that such effortsmay be used, in the future, to validate the formerly new version of thesegment.

FIG. 6 is a flow diagram showing an example of determining a validatedsegment. In some embodiments, process 600 is performed at system 100 ofFIG. 1. In some embodiments, steps 502, 504, and 506 of process 500 ofFIG. 5 are performed using process 600.

At 602, a stored segment to validate is determined. For example, thestored segment is determined to have met a validation criterion.

At 604, a plurality of sets of GPS data associated with matching thestored segment is obtained. In some embodiments, the plurality of setsof GPS data associated with matching the stored segment is associatedwith the efforts that have been determined to match the stored segment.

At 606, a set of GPS data from the plurality of sets of GPS data isidentified. In some embodiments, the set of GPS data associated with oneof the matching efforts is identified based on one or more factors. Eachfound matching effort may be thought as a candidate effort and theidentified effort may be referred to as the “best candidate effort.” Forexample, tests may be applied to each of a subset of the candidateefforts that are recorded by a selected device (e.g., a device beingselected for being known to record with relatively more accuracy) andthe candidate effort associated with the highest score based on thegiven tests is identified. In some embodiments, the candidate effortidentified as the “best candidate effort” is the effort that isdetermined to likely be the most accurate effort (e.g., the effort thatwas recorded with the most accuracy). In some embodiments, the bestcandidate effort is identified by “walking along” each candidate effortand measuring a count of found GPS data points associated with othercandidate efforts within a given radius of each GPS data point alongthat candidate effort. Each GPS data point is of standard distanceinterval from the previous and the next GPS data point from start to endof the candidate effort. A sum is determined for each candidate effortbased on adding together the count of found GPS data points within thegiven radius of each of the candidate effort's GPS data points. Thecandidate effort that has the highest sum is determined to be the bestcandidate effort because it is closest to a centroid of the group ofcandidate efforts and as will be described below, the validated segmentis determined by finding centroids associated with the group ofcandidate efforts.

At 608, a centroid using at least the plurality of sets of GPS data fora GPS data point associated with the identified set of GPS data isdetermined. In some embodiments, each GPS data point of the bestcandidate effort is “walked along” to determine a centroid based onnearby GPS data points associated with at least some other candidateefforts. For example, for a given GPS data point of the best candidateeffort (e.g., starting with the first recorded GPS data point), a radiusof a certain distance is swept out around this GPS data point and atleast some GPS data points associated with other candidate effortswithin the swept out circle are used to determine a centroid. In someembodiments, only a subset of all candidate efforts is considered invalidating the segment. In a specific example, only candidate effortsthat were recorded by a selected device (e.g., a Garmin Edge® 800) areconsidered in validating the segment and so in determining a centroidfor each GPS data point included in the best candidate effort, only GPSdata points of efforts recorded by the selected device that are includedin the radius of a certain distance from the GPS data point are used tofind the centroid.

In some embodiment, the centroid technique also filters out GPS datapoints that are not going in the same general direction as the currentbest candidate effort GPS data point that is under consideration (i.e.,GPS data points of candidate efforts that are not the best candidateefforts are not used to determine the centroid if they are not in thesame general direction as the best candidate effort GPS data point).This way, when there is a tight hairpin turn such that GPS data pointsblend together from both sides of the hairpin, only the GPS data pointsgoing in the same general direction of the best candidate effort GPSdata point are considered—therefore avoiding using GPS data points thatare going in the wrong direction and making for a correct path aroundthe turn. In some embodiments, the technique of finding a centroid isiterated through a predetermined number of times (e.g., four times) todetermine the centroid. The rational for the multiple iterations is thata main corridor could be initially only touched by the area of thecircle around the GPS data point of a best candidate effort—such thatthe centroid of those found GPS data points is still outside of the maincorridor of GPS data points. It is found that multiple iterations enablethe centroid to “march” to the corridor, and once there—it will staythere.

At 610, it is determined whether there are more GPS data points in theidentified set. In the event that it is determined there are more GPSdata points included in the best candidate effort, control passes to612. Otherwise, control passes to 614.

At 612, the next GPS data point in the identified set of GPS data isconsidered and control returns to 608. The next GPS data point (e.g.,the GPS data point recorded next in time) of the best candidate effortis considered until each GPS data point of the best candidate effort iswalked along to create a new centroid based on step 608.

At 614, a validated segment is generated based at least in part on theone or more determined centroids. In some embodiments, the centroidscreated based on walking along each GPS data point of the best candidateeffort are connected to form the new version of the segment that is toreplace the previous version.

FIGS. 7 through 13 illustrate an example of performing process 600 ofFIG. 6 to validate a segment.

FIG. 7 is a diagram showing an example of visual representations of asegment and efforts that have been determined to match the segment. Asegment may be represented by a series of discrete GPS data points. Forexample, GPS data point 712 is associated with segment 702. For example,segment 702 may have been defined based on the GPS data associated witha particular user's uploaded effort. The user may have recorded aneffort and indicated (e.g., via a user interface) a portion of the GPSdata associated with the effort to store as segment 702. Each effortalso corresponds to a series of discrete GPS data points. For example,GPS data point 714 is associated with effort 704. As shown in theexample, the GPS data points of segment 702 are plotted in atwo-dimensional space (associated with latitudinal and longitudinalcoordinates). The GPS data points of four user uploaded efforts (effort704, effort 706, effort 708, and effort 710) that have been determinedto match segment 702 are also plotted in the same space. If it isassumed that it is determined that a validation process is to begin onsegment 702 (e.g., because segment 702 has met a validation criterion),then at least some of the efforts that have been determined to matchsegment 702 are used to validate segment 702. In the example, all fourmatching efforts, effort 704, effort 706, effort 708, and effort 710,are used to validate segment 702. Each of effort 704, effort 706, effort708, and effort 710 may be recorded by the same or different GPS-enableddevices. In some embodiments, only matching efforts that are recorded bya selected type of GPS-enabled device are used to validate the segment(e.g., because the selected type of GPS-enabled device is determined tobe relatively more accurate).

For example, segment 702 may have been defined based on user Steve'suploaded effort associated with a bike ride. Steve may have recorded thebike ride and indicated (e.g., via a user interface) a portion of thegeography covered by the bike ride to store as segment 702. For example,Steve may input the identifier of “Montalvo hill climb” for segment 702to denote the name of the popular climb that he biked over and thegeographical track that segment 702 is intended to represent. Over time,other users have recorded various efforts that have been determined tomatch segment 702 and such efforts include at least effort 704, effort706, effort 708, and effort 710.

FIG. 8 is a diagram showing an example of selecting a best candidateeffort. Continuing the example of FIG. 7, in FIG. 8, one of the matchingefforts (candidate efforts) is identified as the best candidate effort.For example, an effort may be identified as the best candidate effort asa result of determining that the effort is associated with the highestsum of the counts of GPS data points in the vicinity of each of its GPSdata points. The best candidate effort is to serve as a reference effortto use in determining the validated segment (i.e., the new version ofsegment 702). In the example, effort 708 is determined to be the bestcandidate effort.

FIG. 9 is a diagram showing the determination of a centroid based on aGPS data point of the best candidate effort. As described above, eachGPS data point of the best candidate effort may be used to form thecenter of a circular area (e.g., of a predetermined radius) for which atleast a subset of GPS data points associated with the other consideredmatching efforts (and in some embodiments, the original segment, segment702) enclosed in the circular area is used to determine a centroid. Thecentroid is associated with its own set of Lat-Lng coordinates. In someembodiments, a centroid determination function may be applied apredetermined number of times (e.g., four) on the GPS data pointsincluded in the circular area centered on the current GPS data point ofthe best candidate effort for such GPS data points to converge into acentroid. In the example, GPS data point 902 of the best candidateeffort, effort 708, is the current GPS data point around which thecircular area 904 is drawn. The GPS data points of the other efforts(effort 704, effort 706, and effort 710) that are included in circulararea 904 are used to compute a centroid.

FIG. 10 is a diagram showing a centroid determined based on a GPS datapoint of the best candidate effort. Continuing the example of FIG. 9, inFIG. 10, centroid 1002 is computed based on at least a subset of the GPSdata points of the other efforts (effort 704, effort 706, and effort710) that are included in circular area 904. Each subsequent GPS datapoint of the best candidate effort can be similarly used to determine acorresponding centroid.

In some embodiments, in determining the centroid associated with thefirst GPS data point of the best candidate effort, such as GPS datapoint 902 of the best candidate effort in the example, only the firstdata point of each of the other candidate efforts (e.g., such as GPSdata point 1004 of effort 710) are considered so that the centroid willfall along the same start plane as the original segment, segment 702.Making sure that the centroid falls along the same start and end planesas the segment before the validation will help preserve the length ofthe segment.

FIG. 11 is a diagram showing various centroids determined based oncorresponding GPS data points of the best candidate effort. Continuingthe example of FIG. 10, in FIG. 11, subsequent GPS data points in thebest candidate effort are walked along and used to determine acorresponding centroid. In the example, a centroid corresponding to GPSdata point 1102 of the best candidate effort is currently beingdetermined using at least circular area 1104.

FIG. 12 is a diagram showing determined centroids corresponding to allthe GPS data points of the best candidate effort. Continuing the exampleof FIG. 11, FIG. 12 shows corresponding centroids having been determinedfor each GPS data point of the best candidate effort. In someembodiments, in determining the centroid associated with the last GPSdata point of the best candidate effort, only the last GPS data point ofeach of the other matching efforts are considered so that the centroidwill fall along the same end plane as the original segment, segment 702.

FIG. 13 is a diagram showing a validated segment determined based on thedetermined centroids. Continuing the example of FIG. 12, FIG. 13 showsall the determined centroids corresponding to each GPS data point of thebest candidate effort. Such centroids can be stored to represent thevalidated segment, segment 1202, which is the new version of the segmentthat was validated, segment 702. In some embodiments, segment 1202 maybe stored with at least some of the metadata that was previouslyassociated with segment 1202 (e.g., the identifier of “Montalvo hillclimb”) such that segment 1202 can replace segment 702. Existing and newefforts can be compared to segment 1202 to determine matching efforts.

Another example of generating a validated segment, which is not shown,includes finding an average of all the GPS data points included in eachcircular area centered on each GPS data point of the best candidateeffort. Yet another example of generating a validating segment, which isnot shown, includes inputting the GPS data associated with the segmentto be validated and the matching efforts in a GIS program. The GPS datacan be converted into a raster image. Different colors may be applied toareas of the raster image with different densities. For example, theareas with the highest densities may be colored in white. The whiteportions of the raster image may be connected to form the validatedsegment.

Embodiments of repairing recorded GPS data are described herein. In someembodiments, recorded GPS data may include the GPS data associated withefforts. In some embodiments, recorded GPS data may include the GPS dataassociated with segments. As mentioned above, several types of errorsmay corrupt recorded GPS data. Examples of sources of GPS data recordingerrors include the GPS-enabled device losing connection with one or moresatellites that it needs to accurately track Lat-Lng Data, theGPS-enabled device measuring data too infrequently, and the GPS-enableddevice having a limited degree of accuracy such that it misinterpretsLat-Lng Data. As such, because efforts are recorded with GPS-enableddevices, efforts may include inaccurate GPS data. Furthermore, in someembodiments, a segment may be defined based on the set of GPS dataassociated with an effort and may therefore inherit the inaccurate GPSdata that was associated with the effort.

As will be described in more detail below, a first type of GPS datarelated error in an effort is associated with one or more stuck (or“sticky”) GPS data points that occur when the GPS-enabled device cannotdetermine its new location compared to the immediately previous locationand subsequently re-records the same location at successive GPS datapoints, stacking them on top of each other. A second type of GPS datarelated error in an effort includes a drift in at least some of the GPSdata points (e.g., from plausible locations that the GPS data pointsshould have been).

It would be desirable to correct at least some error in the GPS dataassociated with an effort so that the effort will more likely bedetermined to match the segment(s) that it should match. Furthermore, itwould be desirable to correct error in the GPS data associated with aneffort so that the corrected efforts may be used to more accuratelydetermine “best performance portions,” which include determining atleast a portion of an effort associated with a predetermined distancethat is associated with the user's best performance based on aparticular measurement (e.g., speed) as compared with the rest of theeffort. It would be desirable to correct error in the GPS dataassociated with a segment so that more efforts that should match thesegment will more likely be determined to match the segment.Additionally, it would be desirable to correct the GPS data associatedwith the segment so that the segment may better reflect the actualgeographical track that it was defined to represent.

In various embodiments, recorded GPS data is determined based on arepaired base map. In some embodiments, the repaired base map is createdwith one or more different data sources and is used to representaccurate GPS data that efforts and/or segments are to be changed, atleast in part, to reflect.

FIG. 14 is a diagram showing an embodiment of a system for repairing GPSdata. In the example, system 1400 includes device 1402, network 1404,and GPS data repair server 1406. Network 1404 may include high-speeddata networks and/or telecommunications networks. Device 1402 maycommunicate with GPS data repair server 1406 over network 1404.

Device 1402 is a device that can record GPS data and/or other dataassociated with a physical activity. Device 1402 can also be a device towhich GPS data and/or other data associated with a physical activity canbe uploaded or transferred. Examples of device 1402 include, but is notlimited to: a GPS device (e.g., Garmin Forerunner® and Edge® devices,including Garmin Forerunner® 110, 205, 301, 305, 310XT, 405, 405CX, andGarmin Edge® 305, 605, 705, 500, 800), a mobile phone, such as a smartphone (e.g., an Android®-based device or Apple iPhone® device) includinga GPS recording application (e.g., MotionX®, Endomondo®, andRunKeeper®), a computer, a tablet device, and/or other general purposecomputing devices and/or specialized computing devices, which typicallyinclude a general processor, a memory or other storage component(s), anetwork or input/output (I/O) capability, and possibly integrated GPSfunctionality or support or an interface for a GPS device or GPSfunctionality.

In various embodiments, device 1402 (or an application thereof) isconfigured to record GPS data and auxiliary data associated with aphysical activity during the activity. As described above, a set of GPSdata and auxiliary data may be referred to as an effort. For example,auxiliary data associated with a physical activity may includephysiological, environmental, and/or performance data. In someembodiments, device 1402 is configured to receive recorded GPS data andauxiliary data associated with a physical activity subsequent to thecompletion of the activity (e.g., such information is uploaded to device1402).

Similarly as described for system 100 of FIG. 1, a segment may bedefined based on the GPS data associated with an effort (e.g., using auser interface). Segments and efforts may be stored at GPS data repairserver 1406. GPS data repair server 1406 is configured to create arepaired base map using GPS data aggregated from one or more sources.GPS data repair server 1406 is further configured to repair an effortusing the repaired base map. In some embodiments, GPS data repair server1406 is configured to identify a particular type of error associatedwith an effort and repair the effort based on the identified type oferror and the repaired base map. In some embodiments, GPS data repairserver 1406 is configured to determine a best performance portion of arepaired effort. In some embodiments, GPS data repair server 1406 isalso configured to repair a segment using the repaired base map. In someembodiments, GPS data repair server 1406 is configured to determinewhether a repaired effort matches one or more segments, including therepaired segments.

Performance data may be more accurately determined from a repairedeffort and the repaired effort will more likely be determined to match asegment so that comparisons against other efforts that match the samesegment may be performed. Furthermore, a repaired segment may betterrepresent the actual geography of the track that it is intended torepresent.

FIG. 15 is a diagram showing an example of a GPS data repair server. Insome embodiments, GPS data repair server 1406 of system 1400 of FIG. 14is implemented using the example of FIG. 15. In some embodiments, a GPSdata repair server may include more or fewer components than what isshown in the example of FIG. 15. The shown example of the GPS datarepair server includes segments database 1502, efforts database 1504,base maps database 1506, error detection engine 1508, base map repairengine 1510, and GPS data repair engine 1512. Each of segments database1502 and efforts database 1504 may be implemented using one or moredatabases. Each of error detection engine 1508, base map repair engine1510, and GPS data repair engine 1512 may be implemented using one orboth of hardware and software.

Segments database 1502 is configured to store segments. In someembodiments, segments database 1502 may be implemented using segmentsdatabase 202 of the example of the segment validation server of FIG. 2.In some embodiments, a segment stored at segments database 1502 may berepaired by GPS data repair engine 1512. In some embodiments, therepaired version of the segment is used to replace the previous versionof the segment at segments database 1502, regardless of whether theprevious version of the segment has been validated or not. In someembodiments, the repaired segment is stored with at least some of thesame metadata that was stored with the previous version of the segment.In some embodiments, the repaired segment is stored with a versionnumber associated with the number of times that the segment has beenrepaired by GPS data repair engine 1512.

Efforts database 1504 is configured to store efforts. In someembodiments, efforts database 1504 may be implemented using effortsdatabase 204 of the example of segment validation server of FIG. 2. Insome embodiments, an effort stored at efforts database 1504 may berepaired by GPS data repair engine 1512. In some embodiments, therepaired version of the effort is used to replace the previous versionof the effort at efforts database 1504. In some embodiments, therepaired effort is stored with at least some of the same metadata thatwas stored with the previous version of the effort.

Base maps database 1506 is configured to store GPS data from one or moresources. Base maps database 1506 is also configured to store repairedbase maps. For example, a repaired base map comprises a geographicinformation system (GIS) base layer. In various embodiments, a repairedbase map is configured to represent an accurate map that may serve as areference that at least portions of other GPS data (e.g., associatedwith efforts and/or segments) may be repaired/corrected to reflect. Forexample, a repaired base map includes paths, roads, routes, and/ortrails that are known and assumed to be accurate (by virtue of therepair). In some embodiments, base maps database 1506 is configured tostore GIS base maps from one or more third party sources (e.g.,Topologically Integrated Geographic Encoding and Referencing (TIGER) orOpen Street Map). For example, a base map may include streets, highways,boundaries for census, postal, political areas, rivers, lakes, parks,landmarks, place names, and U.S. geological survey (USGS) raster maps.In some embodiments, base maps database 1506 is configured to storecrowd sourced attributes associated with paths/routes that may be usedto modify portions of a generated repaired base map or the repaired basemap's metadata. For example, crowd sourced attributes may indicate thata certain road is a “dirt road,” which could be stored as metadataassociated with the road. Also for example, crowd sourced attributes mayalso indicate unnecessary data (for the purpose of GPS data correction)in the repaired base map such as highways, ferry routes, and roads thatare not typically used for physical activities associated with efforts.

In some embodiments, base maps database 1506 is configured to store GPSdata associated with user uploaded GPS data, such as the GPS dataassociated with efforts. In some embodiments, the user uploaded GPS datais associated with only matching efforts (i.e., efforts that have beendetermined to match at least one segment).

In some embodiments, base maps database 1506 is configured to store GPSdata associated with segments that have been validated, as describedabove. In some embodiments, base maps database 1506 is configured to notredundantly store user uploaded GPS data, such as efforts and/orsegments that are already stored and are accessible at efforts database1504 and segments database 1502.

In some embodiments, base maps database 1506 is configured to storerepaired base maps. In some embodiments, base maps database 1506 isconfigured to store a repaired base map that is generated by base maprepair engine 1510. In some embodiments, a repaired base map isgenerated based on the GPS data aggregated from one or more sources. Forexample, the third party source GPS data, user uploaded GPS data (e.g.,associated with matching efforts), and/or GPS data associated withvalidated segments may be used to determine a repaired base map. In someembodiments, base maps database 1506 is configured to store differentversions of a repaired base map. For example, as more and more updatedGPS data is received over time, updated repaired base maps may begenerated and stored by base maps database 1506 with associated metadatathat indicate their associated versions. Furthermore, each repaired basemap stored by base maps database 1506 may be associated with aparticular type of physical activity. Examples of types of physicalactivity include running and biking For example, using crowd sourcedattributes, for example, it can be determined that certain paths on amap are often used by runners but not by cyclists and vice versa, and soa repaired base map that is optimized for runners (e.g., the repairedbase map can be generated based on user uploaded GPS data associatedwith running) is stored with metadata indicating such so that it may beused later to appropriately correct an effort associated with running.

Error detection engine 1508 is configured to detect error associatedwith efforts and segments. In some embodiments, error detection engine1508 is configured to detect a type of error associated with an effort.For example, types of errors associated with an effort include theeffort having one or more stuck or sticky GPS data points and the efforthaving one or more drifted GPS data points. For example, error detectionengine 1508 may be configured to determine that an effort includes oneor more stuck or sticky GPS data points when speed and acceleration of aportion of an effort is very low, or zero, followed by the leapfrogeffect when the GPS-enabled device re-acquires a good location—spanningthe entire distance covered while all the stuck points were recordedonly in the timespan of the last two recorded points. For example, errordetection engine 1508 may be configured to determine that an effortincludes one or more drifted GPS data points based on receiving a userselection that the recorded effort shows a clear drift from the road orpath based on a user's manual inspection of a visual representation ofthe effort on a map. Also for example, error detection engine 1508 maybe configured to determine that an effort includes one or more driftedGPS data points based on detecting any GPS data points of the effortthat do not fall within corridors of known roads or trails that areincluded in the repaired base map—such GPS data points will beconsidered to have drifted (unless it is determined that there is enoughcrowd sourced attributes to determine that a road or trail actually isthere—in which the road or trail will be automatically recognized anddigitized into the repaired base map).

In some embodiments, error detection engine 1508 may be configured todetermine an effort that should have matched a segment but did not basedon automatically determining that if the effort went from point A topoint B, then it must have covered a segment that is associated withpoints A and B. Additionally, error detection engine 1508 may beconfigured to determine an effort that should have matched a segment butdid not based on receiving a user selection that the user associatedwith the effort knowingly traversed a segment but the effort did notmatch the segment. Furthermore, in some embodiments, error detectionengine 1508 is configured to detect for an effort that the GPS-enableddevice stopped recording a physical activity because of bad signalquality.

In some embodiments, error detection engine 1508 is configured to detecta segment with low quality matches. For example, an effort may bedetermined to match a segment if it is determined that the GPS dataassociated with the effort meets or exceeds a predetermined match (e.g.,similarity) threshold with the GPS data of the segment. An effort thatis associated with GPS data that just barely meets the predeterminedmatch threshold with the GPS data of a segment may be considered to be alow quality match. For example, an effort that is considered to be a lowquality match may be an effort that just meets or exceeds thepredetermined match threshold within a configured range that is abovethe predetermined match threshold. For example, if the predeterminedmatch threshold is 80% and an effort is determined to have a percentageof match of 82% with the segment and the configured range of low qualitymatch is 0% to 5% above the threshold, then the effort is determined tobe a matching effort to that segment (i.e., data indicating a matchingrelationship between the effort and the segment is to be stored) but theeffort is considered to be a low quality match because its percentage ofmatch with the segment is within the configured range of low qualitymatch above the predetermined match threshold. If it appears thatseveral efforts that are determined to match a segment each matches witha low quality of match, then the scenario could indicate that thesegment should be repaired due to the general poor quality of matches.Therefore, in some embodiments, error detection engine 1508 isconfigured to detect that a segment is associated with low qualitymatches if at least a threshold number of efforts that have beendetermined to match the segment each matches with a low quality ofmatch.

Base map repair engine 1510 is configured to generate repaired basemaps. In some embodiments, base map repair engine 1510 is configured togenerate a repaired base map using aggregated GPS data from one or moresources. In some embodiments, base map repair engine 1510 is configuredto use GPS data aggregated at base maps database 1506. Examples ofaggregated GPS data include GPS data from a third party source (e.g.,TIGER), GPS data that is uploaded by users, and/or GPS data associatedwith validated segments. In some embodiments, base map repair engine1510 is configured to use a base map from a particular source (e.g.,TIGER) as an initial base map and repair at least portions of theinitial base map using GPS data from other sources (e.g., user uploadedGPS data). For example, unnecessary portions of the initial base map maybe removed based on the lack of user uploaded GPS data that covers them.For example, only a subset of the user uploaded data that is recorded bya selected GPS-enabled device (e.g., a device that is known to berelatively more accurate than other devices) may be used to repair theinitial base map. In another example, user uploaded data to use torepair a base map may be determined as follows: the body of useruploaded data is mined at a volume corresponding to the sum distance ofroads and trails within each area. A county, for example, may have 5,000kilometers of roads—a specific number of user uploaded GPS datacorresponding to that county will be selected to enable repair of thatarea of the base map. In some embodiments, a weighted average anddensity corridor threshold technique is used to determine the likelypath of a road of trail on the base map using the subset of the useruploaded data. Therefore, portions of the initial base map may beaugmented to generate the repaired base map without having to create abase map from scratch. In some embodiments, base map repair engine 1510is configured to modify a repaired base map using crowd sourcedattributes. For example, portions of the base map may be removed asbeing irrelevant or unnecessary (e.g., for a particular type of physicalactivity) using crowd sourced attributes associated with paths/routes.Also, for example, new paths or routes may be added to a repaired basemap using crowd sourced attributes that confirm their existence.

In some embodiments, base map repair engine 1510 is configured togenerate physical activity type-specific repaired base maps. Forexample, different types of physical activities may entail differenttypes of paths and trails usage on a map. For example, runners often runon paths and trails that cyclists do not use and vice versa. Also,runners tend to run on paths next to roads and cyclists ride on roads.For example, user uploaded GPS data may be physical activitytype-specific. As such, base map repair engine 1510 may generate arepaired base map for each type of physical activity by, in someembodiments, repairing an initial base map created by a third partysource based on physical activity type-specific data. For example, togenerate a repaired base map associated with the physical activity typeof running, the initial base map created by a third party source may berepaired using user uploaded GPS data that is specific to runningactivities. Also, for example, to generate a repaired base mapassociated with the physical activity type of cycling, the initial basemap created by a third party source may be repaired using user uploadedGPS data that is specific to cycling activities.

GPS data repair engine 1512 is configured to repair efforts. In someembodiments, GPS data repair engine 1512 is configured to determine torepair an effort based on the type of error that has been identified forthe effort by error detection engine 1508. In some embodiments, GPS datarepair engine 1512 is configured to repair an effort based on the typeof identified error associated with the effort and a relevant repairedbase map from base maps database 1506. For example, if the effort wasrecorded during a run, then GPS data repair engine 1512 would repair theeffort based on a repaired base map associated with running Examples ofrepairing efforts associated with different types of errors aredescribed further below.

In some embodiments, GPS data repair engine 1512 is configured to repairsegments. In some embodiments, GPS data repair engine 1512 is configuredto determine to repair a segment that has been determined by errordetection engine 1508 to be associated with low quality matches. In someembodiments, GPS data repair engine 1512 is configured to repair asegment based on a repaired base map. Examples of repairing segments aredescribed further below.

In some embodiments, GPS data repair engine 1512 is configured todetermine an end point for an effort that was poorly recorded. In theevent that a GPS-enabled device stops recording a physical activitybecause of bad signal quality, the end point of the effort will need tobe accurately interpolated. In the event that an end point is to beinterpolated for an effort, in some embodiments, GPS data repair engine1512 is configured to analyze the known progress of the recorded effortto the point that the GPS-enabled device stopped recording, as well asaccounting for the next known recorded point. GPS data repair engine1512 may determine that the effort matches at least a portion of asegment and given enough data along the segment to which the effort isdetermined to match, a calculated projected finish time with apercentage accuracy dependent on the amount of data included in theeffort can be provided to a user. For example, given enough data alongthe segment may refer to a certain percentage of the segment that hasbeen matched, which enables an increasingly accurate calculated finishtime the more of the segment that is matched before the GPS-enableddevice stopped recording.

FIG. 16 is a flow diagram showing an embodiment of a process forrepairing an effort. In some embodiments, process 1600 is implemented atsystem 1400 of FIG. 14.

At 1602, it is determined that an effort includes inaccurate GPS data.In some embodiments, an effort that includes inaccurate GPS data isdetermined by detecting patterns and/or signatures of errors in theeffort. For example, different patterns or signatures of errors may beassociated with different types of errors. Examples of types of errorsinclude one or more stuck or sticky GPS data points in an effort and oneor more drifted GPS data points in an effort.

At 1604, the effort is adjusted using a repaired base map. In someembodiments, adjusting the effort includes modifying or adding at leastone or more GPS data points into an effort. In some embodiments, aneffort is adjusted based on the type of error that is identified in theeffort and corrected using a repaired base map alone or in conjunctionwith other references. In some embodiments, a repaired base map that isassociated with the same type of physical activity that is associatedwith the effort is used to adjust the effort.

FIG. 17 is a flow diagram showing an embodiment of a process forrepairing GPS data. In some embodiments, process 1700 is performed atsystem 1400 of FIG. 14.

At 1702, a repaired base map is generated based at least in part on oneor more GPS data sources. A repaired base map is determined from GPSdata that is aggregated from one or more sources.

At 1704, a stored segment is repaired using the repaired base map. Asegment is determined to be repaired using the repaired base map. Insome embodiments, the segment has first been determined to be associatedwith low quality matches prior to being repaired with the repaired basemap. In various embodiments, repairing a segment with a repaired basemap includes moving at least some of the GPS data points in the set ofGPS data associated with the segment or inserting GPS data points intothe set of GPS data associated with the segment based on known pathsincluded in the repaired base map. Because the repaired base map isconsidered to represent accurate GPS data, correction of a segmentincludes changing the segment to better resemble a known path includedin the repaired base map.

At 1706, a type of error associated with an effort is determined.

At 1708, the effort is repaired based at least in part on the determinedtype of error and the repaired base map. The effort is repaired in amanner that is associated with the determined error. In someembodiments, repair associated with a particular type of errorassociated with an effort may include removing one or more GPS datapoints associated with the effort. In some embodiments, the unrepairedeffort is replaced by the repaired effort. Repairing the effort alsoincludes using the repaired base map. In various embodiments, repairingan effort with a repaired base map includes moving at least some of theGPS data points in the set of GPS data associated with the effort orinserting new GPS data points into the set of GPS data associated withthe effort based on known paths included in the repaired base map.Because the repaired base map is considered to represent accurate GPSdata, correction of the effort includes changing the effort to betterresemble a known path included in the repaired base map.

At 1710, it is optionally determined whether the repaired effort matchesany of a plurality of segments including the repaired segment. Therepaired effort is matched against stored segments to determine which ofthe segments (repaired or not) the repaired effort is determined tomatch. The repaired effort may match segments that the unrepaired effortdid not match or even not match segments that the unrepaired effort didmatch. Either way, it is assumed that the repaired effort is a moreaccurate version of the effort and so identifiers of segments that matchthe repaired effort are stored with the effort.

At 1712, a best performance portion associated with the repaired effortis optionally determined. In various embodiments, a best performanceportion refers to a portion associated with a predetermined distance(e.g., 3 kilometers, 1 mile) of an effort that is associated with theuser's best performance with respect to at least one measurement (e.g.,speed, acceleration). For example, the best performance portion mayrefer to the user's fastest 1 mile distance along the recorded run. Thebest performance portion may be thought of as determined by moving asliding window of the predetermined distance across a user's recordedeffort and determining the portion of the effort that is associated withthe best performance measurement as the best performance portion(associated with that particular performance measurement). In someembodiments, several best performance portions, each associated with adifferent performance measurement, may be determined for a repairedeffort. A repaired effort is more likely to yield a more accuratedetermination of a best performance portion because an error in anunrepaired effort may likely interfere with the best performance portiondetermination and cause the determination to be made inaccurately.

FIG. 18 is a flow diagram showing an embodiment of a process forcreating a repaired base map. In some embodiments, process 1800 isimplemented at system 1400 of FIG. 14. In some embodiments, 1702 ofprocess 1700 of FIG. 17 is implemented using process 1800.

At 1802, it is determined whether user uploaded data is to be includedin aggregated GPS data that is to be used to create the repaired basemap. For example, user uploaded GPS data includes the GPS dataassociated with matching efforts. In the event that user uploaded datais determined to be included, control passes to 1804, and user uploadeddata is included in the aggregated GPS data. Otherwise, control passesto 1806.

At 1806, it is determined whether third party data is to be included inaggregated GPS data that is to be used to create the repaired base map.For example, third party GPS data includes free data received from athird party source. For example, free data may even include a base mapcreated by a third party. In the event that third party data isdetermined to be included, control passes to 1808, and third party datais included in the aggregated GPS data. Otherwise, control passes to1810.

At 1810, it is determined whether crowd sourced attributes associatedwith paths are to be used to modify the aggregated GPS data. Forexample, crowd sourced attributes include data received from onlineforum postings and/or user edited base maps. In the event that crowdsourced attributes are determined to be used, control passes to 1812,and crowd sourced attributes associated with paths are used to modifythe aggregated GPS data. Otherwise, control passes to 1814.

At 1814, a repaired base map is generated using at least the aggregatedGPS data. For example, if the aggregated GPS data included user uploadeddata and third party data that included a third party-created base map,then the third party-created base map be augmented by the user uploadeddata and/or the crowd sourced data to create a repaired base map that isto be used to correct segments and/or efforts. Furthermore, in someembodiments, while not shown in the example, GPS data associated withvalidated segments may also be included in the aggregated GPS data andused to create the repaired base map. In some embodiments, the repairedbase map is modified using the crowd sourced attributes associated withpaths.

FIG. 19 is a diagram showing an example of generating a portion of arepaired base map. The example shows a portion of a base map. Assumethat the base map is an existing base map associated with free thirdparty data created by TIGER. The portion of the base map that is shownin the example is associated with a particular road. The outline of theroad that is represented by TIGER data is represented by road 1906. Useruploaded data that is associated with the same road is also included inthe diagram as road 1904 and is to be used to correct the TIGER data togenerate the repaired data. For example, the user uploaded data mayinclude or be derived from the GPS data of one or more efforts along thegeographic track of the road. To repair the road in the example, theroad indicated by TIGER data, road 1906, is corrected using the roadindicated by user uploaded data, road 1904, which results in repairedroad 1902. Road 1902 and other similarly repaired roads will appear inthe repaired base map. For example, repaired road 1902 may be computedbased on determining weighted averages for the road based on the useruploaded data (road 1904) to use to correct road 1906 that was includedin the existing base map to yield repaired road 1902. In someembodiments, the base map may be associated with a particular physicalactivity and the user uploaded data that is used to correct the roads inthe base map is associated with the same physical activity.

In some embodiments, a road such as the road depicted in FIG. 19 may berepaired using a process similar to process 600 of FIG. 6. The onlydifferences are that a piece of road geometry of a base map to berepaired is identified instead of a segment, and rather than findingmatching efforts as candidates, user uploaded data for each county areused as the candidates. As such, a repaired road may comprise a newversion of the road that is determined based on a set of candidate useruploaded data associated with the county in which the road is located.

In various embodiments, segments are repaired to yield segments thatmore accurately represent real-world geographic tracks, paths, or roads.In some embodiments, a segment to be repaired may or may not have beenpreviously validated based on matching efforts. Repaired segments maylikely match more efforts at higher quality matches and provide anaccurate reference against which multiple efforts that matched the samesegment may be compared to each other. FIG. 20 describes one embodimentof repairing a segment.

FIG. 20 is a flow diagram showing an embodiment of a process forrepairing a segment. In some embodiments, process 2000 is implemented atsystem 1400 of FIG. 14. In some embodiments, 1704 of process 1700 ofFIG. 17 is implemented using process 2000.

At 2002, an inaccurate shape data in a stored segment is determined. Insome embodiments, a stored segment is determined to have an inaccurateshape data if at least a threshold number of efforts that match thesegment each matches with a low quality match. If a threshold number ofefforts that match the segment each matches with a low quality match,then the segment is determined to likely include significant error andcould benefit from error correction.

At 2004, shape data for the stored segment is adjusted based on arepaired base map. In some embodiments, adjusting the shape data (e.g.,the set of GPS data) associated with the stored segment based on arepaired base map includes changing the location of some of the GPS datapoints associated with the stored segment based on known paths or roadsincluded in the repaired base map.

FIG. 21 is a flow diagram showing an embodiment of a process forrepairing a segment. In some embodiments, process 2100 is implemented atsystem 1400 of FIG. 14. In some embodiments, process 2100 is used toimplement process 2000 of FIG. 20.

At 2102, a segment to repair is determined. For example, the segment isdetermined to be repaired if at least a threshold number of efforts thatmatch the segment each matches with a low quality match.

At 2104, a linear path between a start point and an end point associatedwith the stored segment is determined. Each segment includes a startpoint and an end point. In some embodiments, the start point of thesegment coincides with the first GPS data point that was selected to beassociated with the segment and the end point of the segment coincideswith the last GPS data point that was selected to be associated with thesegment. In some embodiments, the original series of GPS data points inbetween the start point and the end point is removed and replaced with alinear path. In some embodiments, the linear path comprises a series ofdiscrete GPS data points.

At 2106, a replacement segment is determined based at least in part onadjusting the linear path based on a path included in a repaired basemap. In some embodiments, a path included in a repaired base map that isclosest to the linear path is determined. Then the location of each ofthe GPS data points associated with the linear path is moved to aclosest location along the determined closest path of the repaired basemap. Moving a GPS data point of the linear path to a location of therepaired base map path may be thought of as “snapping” the GPS datapoint onto the path. Snapping is also to be performed in sequence, suchthat the repaired segments continue in a forward motion around a hairpinturn, for example, and not necessarily snapped to the closest path, asit might be the wrong side of the hairpin. This means that there is adistinction between the closest point along a road or path and theclosest likely point which continues the forward motion of the segment.The shape resulting from adjusting the linear path forms the replacementsegment. In some embodiments, the replacement segment is to replace theprevious version of the segment. In some embodiments, the replacementsegment is stored with at least some of the metadata that was storedwith the previous version of the segment.

At 2108, an effort that matches the replacement segment is determined.Stored efforts are compared against the replacement segment to determinewhether any of them match the replacement segment. More or differentefforts may match the replacement segment than those that matched theprevious version of the segment.

At 2110, validation is performed on the replacement segment based atleast in part on the effort. In some embodiments, the replacementsegment may be validated based on the matching efforts such as in aprocess 600 of FIG. 6.

FIGS. 22A through 22F show examples of repairing a segment using process2100 of FIG. 21.

FIG. 22A shows a visual representation of the GPS data pointscorresponding to a segment being plotted in a two dimensional space.Assume that the space is associated with latitudinal and longitudinalcoordinates. Assume that segment 2200 of the example is determined to berepaired. As shown in the example, segment 2200 includes a start pointand an end point. FIG. 22B shows the removal of all but the start pointand the end point of the segment. FIG. 22C shows that a linear path hasbeen created between the start point and the end point of the originalsegment. In the example, the linear path comprises a series of discreteGPS data points. FIG. 22D shows path 2206, which is included in arepaired base map. Path 2206 is the path in the repaired base map thatis determined to be closest to the linear path. In the example, path2206 happens to cross through the start point and the end point of theoriginal segment. FIG. 22E shows adjusting the linear path based on path2206. In the example, each GPS data point of the linear path is moved tothe closest location of path 2206 such that the adjusted path betweenthe start point and the end point of the original segment mimics theshape of path 2206. FIG. 22F shows the replacement segment. Replacementsegment 2208 represents the GPS data of the replacement segment that isthe result of repairing the original segment, segment 2200 of FIG. 22A.In some embodiments, replacement segment 2208 replaces segment 2200.

In various embodiments, efforts are repaired so that they can morelikely match each segment that they should match. Best performanceportions may also be more accurately determined for repaired efforts.Different types of errors determined in efforts may be handled slightlydifferently. FIGS. 23 through 27 show examples of repairing an effortthat is determined to include various different types of errors.However, the described errors are merely examples and efforts may bedetected to have additional and different types of errors that may berepaired in manners similar to those described herein.

FIG. 23 is a flow diagram showing an embodiment of a process forrepairing an effort that is determined to include stuck GPS data points.In some embodiments, process 2300 is implemented at system 1400 of FIG.14. In some embodiments, 1706 and 1708 of process 1700 of FIG. 17 areimplemented using at least process 2300.

At 2302, a determination that an effort includes a set of stuck GPS datapoints has been made. In some embodiments, an effort may be examined forcertain patterns and signatures associated with particular types oferrors. For example, the type of error associated with a set of stuckGPS data points may be determined by finding a set of GPS data pointsassociated with very low, or zero, speed and acceleration followed bythe leapfrog effort when the GPS-enabled device reacquires a goodlocation—spanning the entire distance covered while all stuck pointswere recorded only in the time-span of the last two recorded points. Avisual representation of a set of stuck GPS data points resemblesseveral GPS data points stacked upon each other.

At 2304, at least some of the set of stuck GPS data points are removedfrom the effort. In some embodiments, the associated data (e.g.,timestamps and corresponding measurements associated with environment,physiology, and/or performance) of the removed GPS data points is alsodiscarded.

At 2306, at least one GPS data point is interpolated linearly between afirst GPS data point of the effort associated with being recorded priorto the set of stuck GPS data points and a second GPS data point of theeffort associated with being recorded after the set of stuck GPS datapoints. GPS data points are interpolated (e.g., inserted) linearlybetween the GPS data point of the effort recorded immediately before theremoved stuck GPS data points and the GPS data point of the effortrecorded immediately after the removed stuck GPS data points. In someembodiments, there are as many GPS data points interpolated as therewere removed during 2304.

At 2308, at least one of the interpolated GPS data points is optionallymoved to a closest path included in a repaired base map. In someembodiments, a closest path included in a relevant repaired base map maybe first identified. Then, each of the interpolated GPS data points maybe sequentially moved (snapped on) to this path. In some embodiments,each of the interpolated GPS data points is moved to a closest locationalong the identified path. In some embodiments, once the interpolatedpoints are moved, the interpolated portion of the effort should resemblethe shape of the identified path included in the repaired base map. Thisway, the shape of the effort should at least conform to an accuraterepresentation of a known path. In some embodiments, at least someauxiliary data (e.g., timestamps corresponding measurements associatedwith environment, physiology, and/or performance) are extrapolated forthe moved interpolated GPS data points.

FIGS. 24A through 24D show examples of repairing an effort with stuckGPS data points using a process such as process 2300 of FIG. 23.

FIG. 24A shows a diagram of a visual representation of an effort andcorresponding timestamps. Effort 2402 represents an effort and includesa series of GPS data points recorded during a physical activity. In theexample, each of timestamps 2404 represents a timestamp corresponding toeach of the GPS data points of effort 2402. For example, timestamp 2408corresponds to the time at which GPS data point 2410 was recorded by aGPS-enabled device. Set of stuck points 2406, which includes multipleGPS data points that are recorded in a stacked manner, are included ineffort 2402. Set of stuck points 2406 is to be repaired, as will bedescribed below. FIG. 24B shows that each of set of stuck points 2406has been removed from effort 2402. The timestamps corresponding to setof stuck points 2406 have also been removed from timestamps 2404. FIG.24C shows linearly interpolated GPS data points in the effort. In theexample, the gap in effort 2402 that was created by the removed set ofstuck points now includes a linear interpolation of several GPS datapoints, set of GPS data points 2412. In other words, set of GPS datapoints 2412 has been linearly interpolated between the GPS data pointrecorded immediately before the first removed stuck GPS data point andthe GPS data point recorded immediately after the last removed stuck GPSdata point. In the example, the same number of GPS data points that wereremoved is interpolated. In the example, corresponding timestamps aregenerated for the interpolated GPS data points. FIG. 24D shows movingthe interpolated GPS data points to a path included in a repaired basemap. In the example, path 2414 represents a path included in a repairedbase map. In the example, each of the interpolated GPS data points issnapped onto a location on path 2414 to form repaired portion 2416. Insome embodiments, each of the interpolated GPS data points is moved to aclosest location on path 2414 to form repaired portion 2416. Afterdetermining repaired portion 2416, effort 2402 is considered to berepaired in some embodiments.

FIG. 25 is a flow diagram showing an embodiment of a process forrepairing an effort that is determined to include drifted GPS datapoints. In some embodiments, process 2500 is implemented at system 1400of FIG. 14. In some embodiments, 1706 and 1708 of process 1700 of FIG.17 are implemented using at least process 2500.

At 2502, a determination that an effort includes a set of drifted GPSdata points has been made. In some embodiments, an effort may beexamined for certain patterns and signatures associated with particulartypes of errors. For example, the type of error associated with a set ofdrifted GPS data points may be determined by manual observation that avisual representation of the effort shows that it clearly deviates froma plausible path or road. In another example, the type of errorassociated with a set of drifted GPS data points may be automaticallydetected if at least a portion of an effort does not fall withincorridors of known paths included in a repaired base map. Drifted GPSdata points in an effort can exaggerate the distance that a useractually traveled during the recorded physical activity.

At 2504, at least one GPS data point of the set of drifted GPS datapoints is moved to a closest path included in a repaired base map. Insome embodiments, a closest path included in a relevant repaired basemap may be first identified. Then, each of the drifted GPS data pointsmay be moved to this path. In some embodiments, each of the drifted GPSdata points is sequentially moved (snapped on) to a closest locationalong the identified path. In some embodiments, once the drifted pointsare moved, the moved portion of the effort should resemble the shape ofthe identified path included in the repaired base map. This way, theshape of the effort should at least conform to an accuraterepresentation of a known path.

FIGS. 26A and 26B show examples of repairing an effort with drifted GPSdata points using a process such as process 2500 of FIG. 25.

FIG. 26A shows a visual representation of a recorded effort withsignificant drift on a map application. In the example, the recordedeffort, as represented by the dark line, is associated with a bike rideso it is easy to observe that the physical activity has drifted becausea portion of the activity appears to take place in the ocean, which isimplausible and also does not fall within a corridor of a known path ina repaired base map. As such, at least the portion of the effortassociated with drift (into the ocean) is to be repaired, as will bedescribed next. FIG. 26B shows an example of a repaired effort. In theexample, the portion of the effort associated with drift (into theocean) is snapped onto a known road of a repaired base map. The road islabeled as “The Embarcadero” in the example and various GPS data pointsof the effort have been moved to locations on the road, therebyrepairing the effort.

FIG. 27 is a flow diagram showing an embodiment of a process forrepairing an effort that did not match a segment but should have matcheda segment. In some embodiments, process 2700 is implemented at system1400 of FIG. 14. In some embodiments, 1706 and 1708 of process 1700 ofFIG. 17 are implemented using at least process 2700.

At 2702, a determination that an effort that did not match a segment butshould have matched the segment has been made. A first example ofdetermining an effort that did not match a segment but should havematched the segment may be automatically performed by determining thatif the effort traversed from point A to point B, then it must have beenunavoidable for the effort to traverse across the segment. A secondexample of determining an effort did not match a segment but should havematched the segment may be determined based on a receipt of anindication from a user associated with the effort that the userknowingly traversed the stored segment that the recorded effort was notdetermined to match.

At 2704, at least one GPS data point of the set of GPS data pointscorresponding to the effort is moved to a closest path included in arepaired base map. In some embodiments, a closest path included in arelevant repaired base map may be first identified. Then, each of atleast some of the GPS data points of the effort may be moved to thispath. In some embodiments, each of the at least some of the GPS datapoints is sequentially moved (snapped on) to a closest location alongthe identified path. In some embodiments, once the points are moved,then at least a portion of the effort should resemble the shape of theidentified path included in the repaired base map. This way, the shapeof the effort should at least conform to an accurate representation of aknown path.

Repaired efforts may be used to more accurately determine userperformances across the effort such as the best performance portion.FIGS. 28A and 28B provide examples of determining a best performanceportion of an unrepaired and a repaired effort, respectively.

FIG. 28A is a diagram showing an example of determining a bestperformance portion of an unrepaired effort. In the example, effort2802, which includes a series of discrete GPS data points, includes setof stuck GPS data points 2806. Set of stuck GPS data points 2806 islikely produced due to the GPS-enabled device losing satellite signaland then regaining the signal when it recorded GPS data point 2808. As aresult, a gap in the GPS data points exists across several timestampsbetween set of stuck GPS data points 2806 and GPS data point 2808.Because the best performance portion of an effort may be thought of as awindow of a predetermined distance associated with the best performance(with respect to a particular measurement such as speed) of the effort,best performance portion window 2800 bounded by the start of window 2810to end of window 2812 may be thought of as a sliding window acrosseffort 2802 that aims to capture a window of best performance. However,due to the gap present in unrepaired effort 2802, if end of window 2812were located inside the gap, then the user would not be adequatelycredited for the distance that he or she traveled while the GPS-enableddevice was generating the stuck GPS points. Similarly, window 2810 couldnot start from inside the gap. Furthermore, even if the best performanceportion could be determined to include the gap inside best performanceportion window 2800, the determination would give an inaccuraterepresentation of the user's performance with respect to that portion ofthe effort. As such, the presence of error such as set of stuck GPS datapoints 2806 in an effort makes it difficult to accurately ascertain abest performance portion of the effort.

FIG. 28B is a diagram showing an example of determining a bestperformance portion of a repaired effort. In the example, effort 2802has been repaired such that the set of stuck GPS data points have beenremoved and replaced with a new set of GPS data points. As a result,best performance portion window 2800 is free to slide across the entireeffort 2802 to determine a portion of the effort associated with thebest performance. In the example, portion 2814 of repaired effort 2802is determined to be the best performance portion of the effort. In someembodiments, if a best performance portion had been determined foreffort 2802 prior to repairing it (e.g., as effort 2802 is depicted inFIG. 28A), then the previous best performance portion determination isreplaced by a new best performance portion determination.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A system to repair an effort, comprising: aprocessor configured to: determine that the effort includes inaccurateGPS data; and adjust the effort using a repaired base map; and a memorycoupled to the processor and configured to store the effort.
 2. Thesystem of claim 1, wherein the repaired base map is generated based atleast in part on one or more of the following: user uploaded GPS data,third party GPS data, and GPS data associated with a validated segment.3. The system of claim 2, wherein the user uploaded GPS data includes aset of GPS data corresponding to another effort.
 4. The system of claim1, wherein determining that the effort includes inaccurate GPS dataincludes determining a type of error associated with the effort andadjusting the effort using the repaired base map further includesrepairing the effort based at least in part on the determined type oferror.
 5. The system of claim 4, wherein in the event the type of errorincludes the effort including a set of stuck GPS data points, repairingthe effort based at least in part on the determined type of errorincludes: removing at least some of the set of stuck GPS data pointsfrom the effort; and interpolating at least one GPS data point linearlybetween a first GPS data point of the effort associated with beingrecorded prior to the set of stuck GPS data points and a second GPS datapoint of the effort associated with being recorded after the set ofstuck GPS data points.
 6. The system of claim 5, wherein repairing theeffort based at least in part on the determined type of error furtherincludes moving at least one of the interpolated GPS data points to aclosest path included in the repaired base map.
 7. The system of claim4, wherein in the event the type of error includes the effort includinga set of drifted GPS data points, repairing the effort based at least inpart on the determined type of error includes moving at least one of thedrifted GPS data points to a closest path included in the repaired basemap.
 8. The system of claim 1, wherein the processor is furtherconfigured to determine whether the adjusted effort matches a storedsegment.
 9. The system of claim 1, wherein the processor is furtherconfigured to determine a best performance portion associated with theadjusted effort, wherein the best performance portion comprises aportion of the effort associated with a best performance with respect toa selected measurement.
 10. The system of claim 9, wherein the bestperformance portion comprises a predetermined distance.
 11. A method torepair an effort, comprising: determining that the effort includesinaccurate GPS data; and adjusting the effort using a repaired base map.12. The method of claim 11, wherein the repaired base map is generatedbased at least in part on one or more of the following: user uploadedGPS data, third party GPS data, and GPS data associated with a validatedsegment.
 13. The method of claim 11, wherein determining that the effortincludes inaccurate GPS data includes determining a type of errorassociated with the effort and adjusting the effort using the repairedbase map further includes repairing the effort based at least in part onthe determined type of error.
 14. The method of claim 13, wherein in theevent the type of error includes the effort including a set of stuck GPSdata points, repairing the effort based at least in part on thedetermined type of error includes: removing at least some of the set ofstuck GPS data points from the effort; and interpolating at least oneGPS data point linearly between a first GPS data point of the effortassociated with being recorded prior to the set of stuck GPS data pointsand a second GPS data point of the effort associated with being recordedafter the set of stuck GPS data points.
 15. The method of claim 14,wherein repairing the effort based at least in part on the determinedtype of error further includes moving at least one of the interpolatedGPS data points to a closest path included in the repaired base map. 16.The method of claim 13, wherein in the event the type of error includesthe effort including a set of drifted GPS data points, repairing theeffort based at least in part on the determined type of error includesmoving at least one of the drifted GPS data points to a closest pathincluded in the repaired base map.
 17. A system to repair a segment,comprising: a processor configured to: determine an inaccurate shapedata in the segment; and adjust shape data for the segment based on arepaired base map; and a memory coupled to the processor and configuredto store the segment.
 18. The system of claim 17, wherein the repairedbase map is generated based at least in part on one or more of thefollowing: user uploaded GPS data, third party GPS data, and GPS dataassociated with a validated segment.
 19. The system of claim 18, whereinthe user uploaded GPS data includes a set of GPS data corresponding toan effort.
 20. The system of claim 17, wherein adjusting shape data forthe segment based on the repaired base map includes: determining alinear path between a start point and an end point associated with thesegment; and determining a replacement segment based at least in part onadjusting the linear path based on a path included in the repaired basemap.
 21. The system of claim 20, wherein the processor is furtherconfigured to determine an effort to match the replacement segment. 22.The system of claim 22, wherein the processor is further configured tovalidate the replacement segment based at least in part on the matchingeffort.
 23. The system of claim 17, wherein determining an inaccurateshape data in the segment is based at least in part on determining thata matching effort to the segment is associated with a low quality matchwith the segment.
 24. A method to repair a segment, comprising:determining an inaccurate shape data in the segment; and adjusting shapedata for the segment based on a repaired base map.
 25. The method ofclaim 24, wherein the repaired base map is generated based at least inpart on one or more of the following: user uploaded GPS data, thirdparty GPS data, and GPS data associated with a validated segment. 26.The method of claim 24, wherein adjusting shape data for the segmentbased on the repaired base map includes: determining a linear pathbetween a start point and an end point associated with the segment; anddetermining a replacement segment based at least in part on adjustingthe linear path based on a path included in the repaired base map. 27.The method of claim 26, further comprising determining an effort tomatch the replacement segment.
 28. The method of claim 27, furthercomprising validating the replacement segment based at least in part onthe matching effort.